A review on Deep Learning approaches in Speaker Identification


Deep learning (DL) is becoming an increasingly interesting and powerful machine learning method with successful applications in many domains, such as natural language processing, image recognition, hand-written character recognition, and computer vision. Despite of its eminent success, limitations of traditional learning approach may still prevent deep learning from achieving a wide range of realistic learning tasks. DL approaches has shown success in speech recognition and speaker identification over traditional approaches such as those that use Mel Frequency Cepstrum Coefficients for feature extraction with Gaussian Mixture Models. However, speaker identification research community are not fully aware of the DL process and its application with respect to speaker identification. This paper is motivated to reduce this knowledge gap and to promote the research of implementing deep learning techniques for speaker identification. In this paper, we present a review of the DL methodologies used for speaker identification and surveys important DL algorithms that can potentially be explored for future works. We categorised the applications of DL for speaker identification according to the process of speaker identification and presented a review of these implementations.

Date:
Tuesday, January 24, 2017
Language:
English
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